The OSS GAS-Rugged (GAS-R) offers unprecedented compute density, performance, and flexibility in the first 10 PetaOPS† AI system for rugged edge computing environments.

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OSS GAS-R features the world’s most advanced accelerator, the NVIDIA® A100 Tensor Core GPU, enabling AI on the Fly® customers to consolidate training, inference, and analytics into a unified, deployable AI solution at the edge. With available rack and flange mounting options, adaptable power subsystem and unheard-of performance in a modest 23” depth aluminum enclosure the GAS-R excels in demanding autonomous driving vehicle and airborne applications with true “Datacenter in the Sky” capability.

AI on the Fly applications require computing power that can perform an enormous amount of calculations per second. Increasing the compute density of each server node dramatically reduces the number of servers required, resulting in huge savings in cost, power, and space consumed, all tenets of edge AI deployments. Whether your application needs enhanced AI model compute power, high-dimension matrix multiplication, loading and transforming large datasets or scale out inference, eight A100 GPUs with 320 GB high-bandwidth memory, interconnected with six NVSwitches using 3rd generation NVLink at 600GB/s aggregate bandwidth will handle just about any task.

The complete OSS GAS-R solution incorporates building blocks across hardware, networking, software, libraries, and optimized AI models and applications from NGC™. Representing the most powerful end-to-end AI and HPC platform for high-end edge applications, it allows researchers to deliver real-world results in the real world instead of in a sterile environment and deploy solutions into production at scale.


  • 8x A100 GPUs with 4.8TB/s Aggregate NVLink Bandwidth 
  • 8U x 23” Depth Enclosure
  • Up to 64 Core AMD Epyc 7002 or Dual Intel Scalable Processors
  • Up to 4TB System Memory
  • Over 200TB PCIe Gen4 NVMe storage
  • Four 200Gb/s NICs
  • System monitoring and control through BMC
  • MIL-STD 810G Tested
  • AC (50-400Hz) and DC power options

† INT8 inference with sparsity